1,852 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Estimating Depth from RGB and Sparse Sensing
We present a deep model that can accurately produce dense depth maps given an
RGB image with known depth at a very sparse set of pixels. The model works
simultaneously for both indoor/outdoor scenes and produces state-of-the-art
dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI
datasets. We surpass the state-of-the-art for monocular depth estimation even
with depth values for only 1 out of every ~10000 image pixels, and we
outperform other sparse-to-dense depth methods at all sparsity levels. With
depth values for 1/256 of the image pixels, we achieve a mean absolute error of
less than 1% of actual depth on indoor scenes, comparable to the performance of
consumer-grade depth sensor hardware. Our experiments demonstrate that it would
indeed be possible to efficiently transform sparse depth measurements obtained
using e.g. lower-power depth sensors or SLAM systems into high-quality dense
depth maps.Comment: European Conference on Computer Vision (ECCV) 2018. Updated to
camera-ready version with additional experiment
Tracking and Mapping in Medical Computer Vision: A Review
As computer vision algorithms are becoming more capable, their applications
in clinical systems will become more pervasive. These applications include
diagnostics such as colonoscopy and bronchoscopy, guiding biopsies and
minimally invasive interventions and surgery, automating instrument motion and
providing image guidance using pre-operative scans. Many of these applications
depend on the specific visual nature of medical scenes and require designing
and applying algorithms to perform in this environment.
In this review, we provide an update to the field of camera-based tracking
and scene mapping in surgery and diagnostics in medical computer vision. We
begin with describing our review process, which results in a final list of 515
papers that we cover. We then give a high-level summary of the state of the art
and provide relevant background for those who need tracking and mapping for
their clinical applications. We then review datasets provided in the field and
the clinical needs therein. Then, we delve in depth into the algorithmic side,
and summarize recent developments, which should be especially useful for
algorithm designers and to those looking to understand the capability of
off-the-shelf methods. We focus on algorithms for deformable environments while
also reviewing the essential building blocks in rigid tracking and mapping
since there is a large amount of crossover in methods. Finally, we discuss the
current state of the tracking and mapping methods along with needs for future
algorithms, needs for quantification, and the viability of clinical
applications in the field. We conclude that new methods need to be designed or
combined to support clinical applications in deformable environments, and more
focus needs to be put into collecting datasets for training and evaluation.Comment: 31 pages, 17 figure
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